Tree-Based Methods for Fuzzy Rule Extraction

Shuqing Zeng, Nan Zhang, and Juyang Weng, Michigan State University

This paper is concerned with the application of a tree-based regression model to extract fuzzy rules from high-dimensional data. We introduce a locally weighted scheme to the identification of Takagi-Sugeno type rules. It is proposed to apply the sequential least-squares method to estimate the linear model. A hierarchical clustering takes place in the product space of systems inputs and outputs and each path from the root to a leaf corresponds to a fuzzy IF-THEN rule. Only a subset of the rules is considered based on the locality of the input query data. At each hierarchy, a discriminating subspace is derived from the high-dimensional input space for a good generalization capability. Both a synthetic data set as well as a real-world robot navigation problem are considered to illustrate the working and the applicability of the algorithm

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